Software Engineer | AI Training Data & Evals Lab

Company Overview
Our client is a fast-growing applied research lab building the data layer for frontier AI. They partner with leading AI labs and enterprises to deliver two things: proprietary, expert-generated datasets, and rigorous evaluation and benchmarking. The goal is for AI systems to get better at real workflows, not just polished demos.

Our client is a fast-growing applied research lab building the data layer for frontier AI. They partner with leading AI labs and enterprises to deliver two things: proprietary, expert-generated datasets, and rigorous evaluation and benchmarking. The goal is for AI systems to get better at real workflows, not just polished demos. Founded in 2025 and backed by a $30M Series A, the company is fully remote.

It works on problems such as turning real-world work into clean training signals, building evaluations for software engineering agents and finance workflows, and proving data quality by measuring actual performance lift.

Your Role
This is not a "pick up tickets and wait for specs" role.

This is a broad builder seat that combines platform engineering with evaluation and experimentation infrastructure. You'll design, build, and run systems in production that researchers and operators depend on every day.

The company is scaling the pipelines, evaluation harnesses, and training environments that make its work repeatable, and it needs engineers who can own a system and deliver reliably.

In your first 30 to 90 days, success means shipping at least one meaningful production improvement and becoming the go-to owner of a core system.

You'll:

Build and maintain evaluation harnesses that measure model and agent performance on real tasks

Improve eval reliability, coverage, and signal quality through better rubrics, task design support, and scoring

Ship tools that let researchers and operators run experiments without reinventing the process each time

Build APIs and backend services that power human-in-the-loop workflows, task routing, and quality checks

Improve the pipelines that turn expert work into structured training and evaluation data

Make systems more observable, scalable, and easier to operate through logging, metrics, and debugging

Write clear, maintainable code, take part in reviews and design discussions, and document decisions so others can build on them

You Bring:

Strong coding fundamentals in Node.js and Type Script

Strong coding ability in Python and/or Go

Experience building and owning production systems such as APIs, services, and pipelines

A solid understanding of distributed systems and engineering trade-offs

Comfort with AWS or GCP and modern infrastructure (containers, Kubernetes)

A track record of shipping and maintaining systems other people rely on, not only prototypes

Strong written communication and comfort working async in a distributed team

Bonus Points:

Experience with evaluation frameworks, experimentation platforms, or ML tooling

Experience with data pipelines or workflow orchestration

Experience building internal platforms for operators or research teams

Experience in early-stage or high-ownership B2B SaaS or platform teams

What's Offered:

Full-time, fully remote role with a LATAM focus and meaningful overlap with U.S. time zones

$7, USD/month, based on experience

Real ownership, with growth into bigger systems, deeper technical leadership, and projects core to how the company scales

A lean, async-first team that values clear writing, sound judgment, and follow-through

Research-adjacent engineering at the frontier of AI, alongside practical platform work

Direct impact on the data and evaluations used by leading AI labs

Interview Process:

Take-home assignment covering practical engineering and how you communicate decisions

Application review by the team

Founding engineer screen, a deep dive on system design, trade-offs, and past ownership

Work trial on real-world work, focused on execution, quality, and collaboration

Offer